code: def get_state_dict(self) -> Dict:
      stream_ids = [
          worker.get_state_stream.remote()
          for worker in self.remote_workers
      ]
      # get the first task id that finished executing.
      [stream_id], stream_ids = ray.wait(
          stream_ids, num_returns=1, timeout=None
      )
      byte_obj = ray.get(stream_id)
      _buffer = io.BytesIO(byte_obj)
      state_dict = torch.load(_buffer, map_location="cpu")
      return state_dict
  
imports: from bigdl.orca.learn.ray_estimator import BaseEstimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca.data.file import enable_multi_fs_save, enable_multi_fs_load
  import io
  import torch
  import ray
  from ray.exceptions import RayActorError
  from abc import abstractmethod, ABCMeta
  from bigdl.orca.learn.pytorch.utils import find_free_port, check_for_failure
  from bigdl.orca.learn.utils import get_driver_node_ip
  from bigdl.dllib.utils.log4Error import invalidInputError, logging
  from typing import Dict
label_desc: Use image node_id 8 for calling the BaseRayEstimator obj's underlying member method code with example usage: obj.get_state_dict() and returns: state_dict